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Machine Learning in Medical Imaging

11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Mingxia Liu

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  2. Pingkun Yan

    1. Rensselaer Polytechnic Institute, Troy, USA

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  3. Chunfeng Lian

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  4. Xiaohuan Cao

    1. United Imaging Intelligence, Shanghai, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12436)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Conference proceedings info: MLMI 2020.

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Table of contents (69 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder Using Resting-State fMRI

    • Dongren Yao, Jing Sui, Erkun Yang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu
    Pages 1-10

  3. Error Attention Interactive Segmentation of Medical Image Through Matting and Fusion

    • Weifeng Hu, Xiaofen Yao, Zhou Zheng, Xiaoyun Zhang, Yumin Zhong, Xiaoxia Wang et al.
    Pages 11-20

  4. A Novel fMRI Representation Learning Framework with GAN

    • Qinglin Dong, Ning Qiang, Jinglei Lv, Xiang Li, Liang Dong, Tianming Liu et al.
    Pages 21-29

  5. Semi-supervised Segmentation with Self-training Based on Quality Estimation and Refinement

    • Zhou Zheng, Xiaoxia Wang, Xiaoyun Zhang, Yumin Zhong, Xiaofen Yao, Ya Zhang et al.
    Pages 30-39

  6. 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies

    • Eva Schnider, Antal Horváth, Georg Rauter, Azhar Zam, Magdalena Müller-Gerbl, Philippe C. Cattin
    Pages 40-49

  7. Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network

    • Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li
    Pages 50-59

  8. Self-recursive Contextual Network for Unsupervised 3D Medical Image Registration

    • Bo Hu, Shenglong Zhou, Zhiwei Xiong, Feng Wu
    Pages 60-69

  9. Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy

    • Yuxin Kang, Hansheng Li, Xin Han, Boju Pan, Yuan Li, Yan Jin et al.
    Pages 70-79

  10. Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows

    • Raghavendra Selvan, Frederik Faye, Jon Middleton, Akshay Pai
    Pages 80-90

  11. Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest

    • Xuan Li, Yuchen Lu, Christian Desrosiers, Xue Liu
    Pages 91-100

  12. A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation

    • Yue Zhang, Jiong Wu, Yilong Liu, Yifan Chen, Ed X. Wu, Xiaoying Tang
    Pages 101-110

  13. Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network

    • Hao Guan, Erkun Yang, Li Wang, Pew-Thian Yap, Mingxia Liu, Dinggang Shen
    Pages 111-119

  14. Robust Multiple Sclerosis Lesion Inpainting with Edge Prior

    • Huahong Zhang, Rohit Bakshi, Francesca Bagnato, Ipek Oguz
    Pages 120-129

  15. Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation

    • Zhuowei Li, Qing Xia, Wenji Wang, Zhennan Yan, Ruohan Yin, Changjie Pan et al.
    Pages 130-138

  16. GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes

    • Megi Isallari, Islem Rekik
    Pages 139-149

  17. Anatomy-Aware Cardiac Motion Estimation

    • Pingjun Chen, Xiao Chen, Eric Z. Chen, Hanchao Yu, Terrence Chen, Shanhui Sun
    Pages 150-159

  18. Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation

    • Xi Fang, Thomas Sanford, Baris Turkbey, Sheng Xu, Bradford J. Wood, Pingkun Yan
    Pages 160-169

  19. LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI

    • Zhenyuan Ning, Yu Zhang, Yongsheng Pan, Tao Zhong, Mingxia Liu, Dinggang Shen
    Pages 170-179

  20. Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation

    • Carlos Tor-Diez, Antonio Reyes Porras, Roger J. Packer, Robert A. Avery, Marius George Linguraru
    Pages 180-188
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Other volumes

  1. Machine Learning in Medical Imaging

Keywords

About this book

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.

The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.


Editors and Affiliations

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Mingxia Liu, Chunfeng Lian

  • Rensselaer Polytechnic Institute, Troy, USA

    Pingkun Yan

  • United Imaging Intelligence, Shanghai, China

    Xiaohuan Cao

Bibliographic Information

  • Book Title: Machine Learning in Medical Imaging

  • Book Subtitle: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

  • Editors: Mingxia Liu, Pingkun Yan, Chunfeng Lian, Xiaohuan Cao

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-59861-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-59860-0Published: 03 October 2020

  • eBook ISBN: 978-3-030-59861-7Published: 02 October 2020

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 686

  • Number of Illustrations: 97 b/w illustrations, 230 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Pattern Recognition, Computer Applications

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